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BAREKENG: Jurnal Ilmu Matematika dan Terapan
Published by Universitas Pattimura
ISSN : 19787227     EISSN : 26153017     DOI : https://search.crossref.org/?q=barekeng
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation
BAREKENG: Jurnal ilmu Matematika dan Terapan is one of the scientific publication media, which publish the article related to the result of research or study in the field of Pure Mathematics and Applied Mathematics. Focus and scope of BAREKENG: Jurnal ilmu Matematika dan Terapan, as follows: - Pure Mathematics (analysis, algebra & number theory), - Applied Mathematics (Fuzzy, Artificial Neural Network, Mathematics Modeling & Simulation, Control & Optimization, Ethno-mathematics, etc.), - Statistics, - Actuarial Science, - Logic, - Geometry & Topology, - Numerical Analysis, - Mathematic Computation and - Mathematics Education. The meaning word of "BAREKENG" is one of the words from Moluccas language which means "Counting" or "Calculating". Counting is one of the main and fundamental activities in the field of Mathematics. Therefore we tried to promote the word "Barekeng" as the name of our scientific journal also to promote the culture of the Maluku Area. BAREKENG: Jurnal ilmu Matematika dan Terapan is published four (4) times a year in March, June, September and December, since 2020 and each issue consists of 15 articles. The first published since 2007 in printed version (p-ISSN: 1978-7227) and then in 2018 BAREKENG journal has published in online version (e-ISSN: 2615-3017) on website: (https://ojs3.unpatti.ac.id/index.php/barekeng/). This journal system is currently using OJS3.1.1.4 from PKP. BAREKENG: Jurnal ilmu Matematika dan Terapan has been nationally accredited at Level 3 (SINTA 3) since December 2018, based on the Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia, with Decree No. : 34 / E / KPT / 2018. In 2019, BAREKENG: Jurnal ilmu Matematika dan Terapan has been re-accredited by Direktur Jenderal Penguatan Riset dan Pengembangan, Kementerian Riset, Teknologi, dan Pendidikan Tinggi, Republik Indonesia and accredited in level 3 (SINTA 3), with Decree No.: 29 / E / KPT / 2019. BAREKENG: Jurnal ilmu Matematika dan Terapan was published by: Mathematics Department Faculty of Mathematics and Natural Sciences University of Pattimura Website: http://matematika.fmipa.unpatti.ac.id
Arjuna Subject : Matematika - Matematika Terapan
Articles 60 Documents
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Search results for , issue "Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application" : 60 Documents clear
CLASSIFICATION ANALYSIS USING BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINE (BAGGING MARS) Rupilu, Rina Apriany Helen Wite; Rosadi, Dedi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1381-1390

Abstract

Classification analysis is a method used to classify or analyze the relationship between several predictor variables and response variables that aim to predict the class of an object whose label is unknown. This classification problem arises when a number of measures consist of one or more categories that cannot be defined directly but use a measure. MARS is one of the classification methods focused on overcoming high-dimensionality and discontinuity problems in data. The accuracy or classification level of the MARS method can be improved using a resampling method, namely bagging. This study will apply the MARS model to obtain a model for classifying the status of people with diabetes based on people with diabetes. The data used in this study is secondary data obtained from the Kaggle website which can be accessed through https://www.kaggle.com/uciml/pima-indians-diabetes-database, namely the Pima Indians Diabetes Database and processed using R software. The results of MARS modeling concluded that the probability of someone having diabetes is 0. The probability of someone not having diabetes is 1, with a classification accuracy of 81.38%. In contrast, the accuracy of the best MARS bagging method among 200 replications is 75.23%, so in this study, a more appropriate method is used to classify the status of people with diabetes.
LOGISTIC AND PROBIT REGRESSION MODELING TO PREDICT THE OPPORTUNITIES OF DIABETES IN PROSPECTIVE ATHLETES Ariyanto, Danang; Sofro, A'yunin; Hanifah, A’idah Nur; Prihanto, Junaidi Budi; Maulana, Dimas Avian; Romadhonia, Riska Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1391-1402

Abstract

Diabetes is among the most prevalent chronic diseases globally, posing significant health risks to individuals. The identification of individuals at risk of developing these conditions is of paramount importance, particularly in high-stress and physically demanding activities such as athletic training. To find out the chances of a prospective athlete suffering from diabetes or not, models for binary data can be used, including logistic regression and probit models. The data used is primary data from prospective athletes in East Java, including prospective athletes from the State University of Surabaya and East Java Koni Athletes. This study aimed to develop an early prediction model for diabetes in prospective athletic candidates using a bivariate logistic and probit regression approach while considering the influence of socio-demographic and anthropometric factors. To selecting the best model between logistic regression and probit regression using Akaike’s Information Criterion (AIC) value, the smaller the AIC value gets means that the model is closer to the actual value or being the best model. Logistic regression has a smaller AIC value (129,85) than probit regression, this means that the logistic model is the best model. In this paper, an attempt is made to explore the use of logistic and probit regression to determine the factors which significantly influence the diabetes disease and we got that the logistic model as the best model because it has a smaller AIC value than the probit model. Based on the result of analysis and discussion, it can be concluded that there are two factors called mother’s job and finance which are influenced to the response variable, diabetes disease at significance level of 5%.
OPTIMIZATION OF WASTE MANAGEMENT IN SUMBAWA DISTRICT USING A DYNAMIC SYSTEM MODEL Susilawati, Tri; Darmawan, Indra; Alfaresa, Muhammad Saiful
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1403-1410

Abstract

The main problem that occurs in society today apart from basic needs is the problem of waste. This waste problem certainly needs a solution in good, effective, sustainable and long-term management. The waste issue not only occur in big cities but also in developing areas. Sumbawa Regency as one of the regions that continues to develop and grow requires optimum and long-term planning of solid waste management and systems. In 2021, the waste handled in Sumbawa Regency will only be around 17.5% of the total waste. This shows that the waste management system in Sumbawa Regency is not optimal. Optimal waste management can be achieved by taking an academic approach through model development. One such model is the dynamic system model. This can describe the causal relationship between variables that affect solid waste problems and to provide an overview of the waste management system through simulations based on the factors that influence the processing model. Therefore, it is necessary to develop an optimization model for waste management specifically in Sumbawa Regency using a dynamic systems approach. The purpose of this study is to describe waste management in the form of an optimization model for waste management in Sumbawa Regency with a dynamic system. Optimization results show that the optimistic model yields a total of 127.01 tons/day, the moderate model yields a total of 98.23 tons/day and the pessimistic model produces 62.5 tons/day
ANALYSIS OF PRIORITY AREAS FOR HANDLING STUNTING CASES IN SIGI REGENCY USING THE TOPSIS METHOD BASED ON WEB DASHBOARD Mu'arif, Zainal; Afriza, Dini Aprilia; Aulia, Firda; Anggelina E, Melsy Patricia; Gamayanti, Nurul Fiskia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1411-1422

Abstract

Stunting is a condition of growth failure in children, where a toddler has a length or height below the average. Stunting is a problem for children because it has the potential to slow down brain development with prolonged effects. Central Sulawesi Province is one of the provinces with the highest stunting prevalence rate and the area with the highest stunting rate is in Sigi Regency at 36.8%. Stunting cases are an important concern for the Sigi Regency government, especially the Health Office and Community Health Centers. To identify and determine areas that are prioritized for handling stunting cases, seven indicators are used, including the number of stunting cases, number of villages covered, number of health workers, number of integrated service posts, number of exclusive breastfeeding, percentage of clean drinking water, and percentage of proper sanitation. To support in reducing the percentage of stunting in Sigi Regency, research was conducted and a web dashboard system application was made to support priority area selection decisions using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, a best alternative method that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The results obtained in this study are the areas that are prioritized for handling stunting cases in Sigi Regency is the Sigi Biromaru area with a total of 495 stunting cases, the number of coverage villages is 18, the number of integrated service posts is 53, the number of health workers is 96, the number of exclusive breastfeeding is 35, the percentage of proper drinking water is 44%, and the percentage of proper sanitation is 84.00% with the highest preference value through the TOPSIS method analysis of 0.660.
IMPLEMENTATION OF INTEGER PROGRAMMING USING THE BRANCH AND BOUND METHOD ASSISTED BY PYTHON IN OPTIMIZING THE PRODUCTION OF COOKIES Saranta, Nira Nityasa; Setiawani, Susi; Prihandini, Rafiantika Megahnia; CahyaPrihandoko, Antonius; Wihardjo, Edy
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1423-1432

Abstract

Many people are interested in cookies. Due to the high consumer interest in cookies, many companies produce cookies with various variants, one of which is Rizky Bakery. The problem faced by Rizky Bakery is how to determine the amount of production of 6 types of cookies to reach the maximum profit. Rizky Bakery carries out production activities to meet high market demand and standard demand. This study constructs a model that accommodates both conditions. The model is solved by using the Branch and Bound method constraints on materials, manufacturing time, fee labor, payment for resellers, and production targets. The purpose of this research is to determine the total number program model and the optimal solution by maximizing the profit of cookie production using Branch and Bound. Optimization using the Branch and Bound method can utilize the Python programming language with a limit of 50 iterations. Data collection methods used for this research are interviews and documentation. The limitation of the problem in this research is that the model to be studied is limited to the average condition of demand is standard and when demand is high. The results of the analysis at times of high demand showed that the production of nastar cookies, castangel cookies, mawar cookies, putri salju cookies, peanut cookies, and custard cookies in 300-gram packaging respectively are 250, 45, 80, 39, 90, 150 and in 500-gram packages are 40, 10, 10, 6, 45, and 45. While, the result of standard demand in 300-gram packaging respectively are 100, 25, 50, 16, 50, 60 and the 500-gram packaging respectively are 10, 3, 10, 2, 10, 20. The profit earned when the demand is high is IDR 8,769,412.00 and the standard demand is IDR 3,769,504.00.
PREDICTION OF PROSPECTIVE NEW STUDENTS USING DECISION TREE, RANDOM FOREST, AND NAIVE BAYES Brianorman, Yulrio; Sucipto, Sucipto
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1433-1446

Abstract

Higher education positions new student enrollment as a strategic activity for private universities. The effectiveness of selecting prospective students with a high potential to register and be accepted is crucial. Therefore, this study was conducted to find a data classification model that can determine the potential acceptance of new students, allowing private universities to increase the number of students admitted. This research's data originated from the 2020 new student admissions at a prominent private university in Pontianak city. The three chosen classification methods are the decision tree, random forest, and naïve-bayes. Evaluation results indicate the accuracy rate of the decision tree is 59.1%, random forest at 59.2%, and naïve-Bayes at 58.1%. Despite similar accuracy rates, the random forest method slightly outperformed the others, suggesting it may be the most reliable for predicting student enrollment. Based on these models, the estimated potential of prospective students registering at the university ranges from 72% to 78% of the total student candidates. In conclusion, although the three models have almost similar accuracy rates, all show an optimistic estimate regarding the registration potential of prospective students. Thus, universities can use one or a combination of the three models to enhance efficiency in the student admission process.
IMPLEMENTATION OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODEL (VECM) METHOD IN PNEUMONIA PATIENTS WITH WEATHER ELEMENTS IN PANGKALPINANG CITY Amelia, Ririn; Wahyuni, Dhiti; Julianti, Erna
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1447-1458

Abstract

The news about Covid-19 is no longer as scary as in previous years. As COVID-19 cases decrease, health protocols are becoming more relaxed, making it easier for the virus to spread. Besides COVID-19, ARI is one of Indonesia's leading causes of death for children under five. Around 20-40% of hospital admissions are children due to ARI, with around 1.6 million deaths due to pneumonia alone in children under five per year. Currently, ARI dominates the diseases most suffered by the people of Bangka Belitung. Based on this, using the VAR and VECM method to analyze pneumonia sufferers in toddlers regarding weather elements in the Pangkalpinang City. The VAR model has a simpler structure with a minimal number of variables where all the variables are endogenous, with the independent variable being the lag. Meanwhile, VECM can be used to model cointegrated and non-stationary time series data. The data used in this research is the number of monthly cases of toddlers suffering from pneumonia and data on climate conditions, namely rainfall, air temperature, air humidity and duration of sunlight during 2019-2021 in Pangkalpinang City. The results of the Granger Causality test show that the pneumonia variables regarding rainfall, temperature, duration of sunlight and humidity only have a one-way causality pattern. The VAR estimation results show that weather elements (rainfall, temperature and duration of sunlight) do not significantly affect pneumonia in the short term. Meanwhile, the VECM estimation results show that in the long-term pattern, humidity variables affect pneumonia. For this reason, it is recommended that the relevant agencies carry out outreach to the public, especially to pneumonia sufferers, to avoid damp weather. Because the lower the humidity value, the greater the potential for pneumonia in Pangkalpinang City, Bangka Belitung Islands Province.
MODELING DHF IN CENTRAL JAVA USING HYBRID NONPARAMETRIC SPLINE TRUNCATED-FOURIER SERIES APPROACH Utami, Tiani Wahyu; Maharani, Endang Tri Wahyuni; Fadlurohman, Alwan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1459-1470

Abstract

Regression analysis aims to determine the relationship and influence of predictor variables on response variables through regression curve. The problem with nonparametric regression research so far is that it only uses one approach, causing the estimation results to be biased, even though each data sub-pattern has its own suitability depending on the approach method used. Therefore, the hybrid method emerged as a development of nonparametric regression. Hybrid models are models that combine approach methods, with the hope of increasing accuracy in modeling analysis. This research was carried out using two non-parametric approaches, namely Spline Truncated and Fourier Series. Dengue Hemorrhagic Fever (DHF) is a disease caused by the dengue virus. DHF is endemic and occurs throughout the year, especially during the rainy season because mosquitoes reproduce optimally. The aim of this research is to estimate the Hybrid Nonparametric Spline Truncated -Fourier Series model and apply the estimation results to data on DHF cases in Central Java. The data used to apply the hybrid nonparametric Spline Truncated-Fourier series regression model is DHF in the city/districts of Central Java. Estimation smoothing parameters uses the GCV (Generalized Cross Validation) method. The best model is selected based on largest R-Square and the smallest MSE. Modeling the disease of DHF cases in Central Java using the Spline Truncated-Fourier Series hybrid estimator produced the best model from the Spline Truncated model with two knot points for each predictor and the Fourier Series model with value of 9. Based on the results obtained, it can be compared that the Truncated Spline-Fourier Series hybrid model is better than the Spline Truncated model, this can be seen from the largest R-square, namely 99.94% and the smallest MSE.
THE SIMPLEX - PREEMPTIVE GOAL PROGRAMMING WITH BRANCH AND BOUND METHOD FOR OPTIMIZING WASTE VEHICLE ROUTES AND TRANSPORTATION Fadhila, Rifa; Rarasati, Niken; Rozi, Syamsyida; Putra, Fernando Mersa
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1471-1482

Abstract

This research discusses the optimization of the assignment of waste vehicle routes and transportation, which are responsible for transporting waste from temporary disposal sites to final disposal sites. The aim is to minimize the remaining waste at temporary disposal sites, thus creating a clean and comfortable environment. The focus of this research is determining the number of trips (rit) for each vehicle (dump trucks and arm roll trucks) on each route. Besides aiming to minimize the remaining waste at temporary disposal sites as the main priority, there are also other priorities, namely minimizing fuel usage and the working time within a day. Therefore, in accordance with the characteristics of the problem, where there are multiple objectives and an integer solution is required, The Simplex-Preemptive Goal Programming with the Branch and Bound method is proposed as the solution method. The optimal solution has been obtained. The result includes the optimal number of trips (rit) for each waste transport vehicles (dump trucks and arm roll trucks) on each routes.
ECONOMIC GLOBALIZATION, ECONOMIC GROWTH, AND HUMAN CAPITAL : EMPIRICAL EVIDENCE USING THREE STAGE LEAST SQUARE IN INDONESIA Huda, Qorinul; Istiana, Nofita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1483-1496

Abstract

The pace of globalization is increasing rapidly and dynamically as time goes by. Indonesia, located takes advantage of globalization to encourage economic growth. However, over the last decade, from 2000 to 2019, Indonesia's economic globalization index has tended to decline along with the increase in the global economic globalization index. Indonesia's economic growth has been relatively stagnant. Human capital, as the primary input in Indonesia's economic system, is suspected to be suboptimal. In the National Medium-Term Development Plan (RPJMN) for 2020-2024, economic growth and human capital are the main focus in achieving national prosperity. Human capital in this study uses health indicators as a proxy for assessing productivity and educational investment approaches. Data is transformed to meet the stationarity requirements of time series data. The study employs the Three Stage Least Square (3SLS) simultaneous equation method to examine total and direct effects. The estimation results show that changes in globalization growth are directly influenced by changes in economic growth, exchange rate growth, and inflation. Changes in economic growth are directly influenced by changes in exchange rate growth, globalization index growth, and inflation. Human capital is directly influenced by changes in globalization index growth, changes in economic growth, inflation, previous-year inflation, and changes in unemployment rates

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